System and Method for Improving Speech Recognition Accuracy Using Textual Context

ABSTRACT

Disclosed herein are systems, methods, and computer-readable storage media for improving speech recognition accuracy using textual context. The method includes retrieving a recorded utterance, capturing text from a device display associated with the spoken dialog and viewed by one party to the recorded utterance, and identifying words in the captured text that are relevant to the recorded utterance. The method further includes adding the identified words to a dynamic language model, and recognizing the recorded utterance using the dynamic language model. The recorded utterance can be a spoken dialog. A time stamp can be assigned to each identified word. The method can include adding identified words to and/or removing identified words from the dynamic language model based on their respective time stamps. A screen scraper can capture text from the device display associated with the recorded utterance. The device display can contain customer service data.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 15/146,283, filed May 4, 2016, which is a continuation of U.S.patent application Ser. No. 14/737,708, filed Jun. 12, 2015, now U.S.Pat. No. 9,355,638, issued May 31, 2016, which is a continuation of U.S.patent application Ser. No. 14/061,855, filed Oct. 24, 2013, now U.S.Pat. No. 9,058,808, issued Jun. 16, 2015, which is a continuation ofU.S. patent application Ser. No. 12/604,628, filed Oct. 23, 2009, nowU.S. Pat. No. 8,571,866, issued Oct. 29, 2013, the contents of theforegoing of which are incorporated by reference in their entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates to speech recognition and morespecifically to improving speech recognition accuracy based on relatedtext. Introduction

INTRODUCTION

Call centers and other voice-based customer service interfaces oftenrecord speech for later data mining to determine trends, customersatisfaction rates, etc. However, automatic speech recognition (ASR)often fails on such recorded speech, produces erroneous recognitionresults, or encounters difficulty when recognizing speech from customerservice and related speech applications because the vocabulary isdifferent from what is regularly expected. While ASR grammar models canbe generally trained for domain-specific tasks, this type of recordedspeech often includes frequently-used words that are beyond thedomain-specific grammar model. Such vocabulary-based difficulties in ASRpresent problems for data mining and other applications of recordedspeech.

SUMMARY

Accurate ASR from voice data alone is a difficult problem. However, insome situations, an ASR system has access to other data beyond the voicedata that makes speech recognition less difficult. In particular, if theother data includes text mentioned in the voice data, then the ASRsystem improve its predictions about what is being said based on thattext. One compelling example is when a contact center agent is talkingon the phone with a customer about a product or service that thecustomer has purchased. An automatic system can record suchconversations and transcribe them into text using ASR for later use indata mining In this context, the agent's computer monitor often containsa great deal of relevant information about the customer and/or theproduct being discussed. Words such as people's names, addresses, otherpersonal information, and product names are often the most difficult forASR systems because they are not in even a domain- or task-specificvocabulary. The system can capture words from the agent's monitor andadd them to the ASR system's “cache language model” to improverecognition accuracy.

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

Disclosed are systems, methods, and computer-readable storage media forimproving speech recognition accuracy using textual context. The methodcan be practiced by a suitably configured system. The system retrieves arecorded utterance, captures text from a device display associated withthe spoken dialog and viewed by one party to the recorded utterance,identifies words in the captured text that are relevant to the recordedutterance, adds the identified words to a dynamic language model, andrecognizes the recorded utterance using the dynamic language model. Therecorded utterance can be a spoken dialog. The system can assign a timestamp to each identified word. The system can add identified words tothe dynamic language model and/or remove identified words from thedynamic language model based on its respective assigned time stamp. Ascreen scraper can capture text from the device display associated withthe recorded utterance. The device display can contain customer servicedata. The captured text can be a name, a location, a phone number, anaccount type, and/or a product name

In one aspect, the system further determines an utterance category basedon the captured text, and adds utterance category specific words to thedynamic language model. In another aspect, the system identifies a userin the dialog, and saves the dynamic language model as a personalizeddynamic language model associated with the identified user. The systemcan then retrieve a second spoken dialog including the identified user,load the personalized dynamic language model associated with theidentified user, and recognize the second spoken dialog using thepersonalized dynamic language model. Adding the identified words to adynamic language model can include rescoring an existing language model.Identifying words in the captured text that are relevant to the recordedutterance can include extracting from the captured text references toexternal data, retrieving the external data, identifying data ofinterest in the parsed data, and adding the identified data of interestto the dynamic language model.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only exemplary embodiments of the disclosure and are nottherefore to be considered to be limiting of its scope, the principlesherein are described and explained with additional specificity anddetail through the use of the accompanying drawings in which:

FIG. 1 illustrates an example system embodiment;

FIG. 2 illustrates an example method embodiment for improving speechrecognition accuracy using textual context;

FIG. 3 illustrates an exemplary system configuration; and

FIG. 4 illustrates an exemplary display of relevant information.

DETAILED DESCRIPTION OF THE DRAWINGS

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the disclosure.

With reference to FIG. 1, an exemplary system 100 includes ageneral-purpose computing device 100, including a processing unit (CPUor processor) 120 and a system bus 110 that couples various systemcomponents including the system memory 130 such as read only memory(ROM) 140 and random access memory (RAM) 150 to the processor 120. Theseand other modules can be configured to control the processor 120 toperform various actions. Other system memory 130 may be available foruse as well. It can be appreciated that the disclosure may operate on acomputing device 100 with more than one processor 120 or on a group orcluster of computing devices networked together to provide greaterprocessing capability. The processor 120 can include any general purposeprocessor and a hardware module or software module, such as module 1162, module 2 164, and module 3 166 stored in storage device 160,configured to control the processor 120 as well as a special-purposeprocessor where software instructions are incorporated into the actualprocessor design. The processor 120 may essentially be a completelyself-contained computing system, containing multiple cores orprocessors, a bus, memory controller, cache, etc. A multi-core processormay be symmetric or asymmetric.

The system bus 110 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. A basicinput/output (BIOS) stored in ROM 140 or the like, may provide the basicroutine that helps to transfer information between elements within thecomputing device 100, such as during start-up. The computing device 100further includes storage devices 160 such as a hard disk drive, amagnetic disk drive, an optical disk drive, tape drive or the like. Thestorage device 160 can include software modules 162, 164, 166 forcontrolling the processor 120. Other hardware or software modules arecontemplated. The storage device 160 is connected to the system bus 110by a drive interface. The drives and the associated computer readablestorage media provide nonvolatile storage of computer readableinstructions, data structures, program modules and other data for thecomputing device 100. In one aspect, a hardware module that performs aparticular function includes the software component stored in a tangibleand/or intangible computer-readable medium in connection with thenecessary hardware components, such as the processor 120, bus 110,display 170, and so forth, to carry out the function. The basiccomponents are known to those of skill in the art and appropriatevariations are contemplated depending on the type of device, such aswhether the device 100 is a small, handheld computing device, a desktopcomputer, or a computer server.

Although the exemplary embodiment described herein employs the hard disk160, it should be appreciated by those skilled in the art that othertypes of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, digital versatile disks, cartridges, random access memories(RAMs) 150, read only memory (ROM) 140, a cable or wireless signalcontaining a bit stream and the like, may also be used in the exemplaryoperating environment. Tangible computer-readable storage mediaexpressly exclude media such as energy, carrier signals, electromagneticwaves, and signals per se.

To enable user interaction with the computing device 100, an inputdevice 190 represents any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. The inputdevice 190 may be used by the presenter to indicate the beginning of aspeech search query. An output device 170 can also be one or more of anumber of output mechanisms known to those of skill in the art. In someinstances, multimodal systems enable a user to provide multiple types ofinput to communicate with the computing device 100. The communicationsinterface 180 generally governs and manages the user input and systemoutput. There is no restriction on operating on any particular hardwarearrangement and therefore the basic features here may easily besubstituted for improved hardware or firmware arrangements as they aredeveloped.

For clarity of explanation, the illustrative system embodiment ispresented as including individual functional blocks including functionalblocks labeled as a “processor” or processor 120. The functions theseblocks represent may be provided through the use of either shared ordedicated hardware, including, but not limited to, hardware capable ofexecuting software and hardware, such as a processor 120, that ispurpose-built to operate as an equivalent to software executing on ageneral purpose processor. For example the functions of one or moreprocessors presented in FIG. 1 may be provided by a single sharedprocessor or multiple processors. (Use of the term “processor” shouldnot be construed to refer exclusively to hardware capable of executingsoftware.) Illustrative embodiments may include microprocessor and/ordigital signal processor (DSP) hardware, read-only memory (ROM) 140 forstoring software performing the operations discussed below, and randomaccess memory (RAM) 150 for storing results. Very large scaleintegration (VLSI) hardware embodiments, as well as custom VLSIcircuitry in combination with a general purpose DSP circuit, may also beprovided.

The logical operations of the various embodiments are implemented as:(1) a sequence of computer implemented steps, operations, or proceduresrunning on a programmable circuit within a general use computer, (2) asequence of computer implemented steps, operations, or proceduresrunning on a specific-use programmable circuit; and/or (3)interconnected machine modules or program engines within theprogrammable circuits. The system 100 shown in FIG. 1 can practice allor part of the recited methods, can be a part of the recited systems,and/or can operate according to instructions in the recited tangiblecomputer-readable storage media. Generally speaking, such logicaloperations can be implemented as modules configured to control theprocessor 120 to perform particular functions according to theprogramming of the module. For example, FIG. 1 illustrates three modulesMod1 162, Mod2 164 and Mod3 166 which are modules configured to controlthe processor 120. These modules may be stored on the storage device 160and loaded into RAM 150 or memory 130 at runtime or may be stored aswould be known in the art in other computer-readable memory locations.

Having disclosed some basic system components, the disclosure now turnsto the exemplary method embodiment shown in FIG. 2. For the sake ofclarity, the method is discussed in terms of an exemplary system such asis shown in FIG. 1 configured to practice the method.

FIG. 2 illustrates an exemplary method embodiment for improving speechrecognition accuracy using textual context. A system configured topractice the method retrieves a recorded utterance (202) and retrievestext captured from a device display associated with the spoken dialogand viewed by one party to the recorded utterance (204). The recordedutterance can be a spoken dialog between two persons, a dialog between aperson and an automated system, or any other type of utterance. Oneexample is a telephone call between a customer and a live and/orautomated customer service agent. In one aspect, a screen scrapercaptures text from the device display associated with the recordedutterance. The screen scraper can be a program running on a computer,such as a customer service terminal. The screen scraper can also be aserver that remotely monitors the device display. In yet anothervariation, the screen scraper can process recorded device display outputat a later time. The screen scraper can recover data from the entiredevice display or an indicated portion of the device display. Someexamples of captured text include a name, a location (such as anaddress), a phone number, an account type, or a product name The systemcan further determine an utterance category based on the captured textand add utterance category specific words to the dynamic language model.For example, if the captured text includes the phrase “AT&T U200 DigitalTelevision”, the system can load specific terminology associated withthat package and also load television channel names included in thatpackage such as LOGO, DIY network, and Bloomberg Television.

The system identifies words in the captured text that are relevant tothe recorded utterance (206) and adds the identified words to a dynamiclanguage model (208). In one embodiment, identified words are assignedtime stamps based on a timing relative to the recorded utterance. Forexample, if the system identifies the word “modem” a corresponding timestamp identifies a position within the recorded utterance where the word“modem” appeared on the device display. Time stamps can indicate a beginand an end time of identified words. The system can add words to thedynamic language model based on a begin time stamp and remove words fromthe dynamic language model based on an end time stamp. When the dynamiclanguage model has a maximum threshold of added words, the system canleave added words in the dynamic language model after the end time stampuntil the dynamic language model is “full” and the system must add newwords. The system can add and remove words from the dynamic languagemodel at the exact time of the timestamp, slightly before, and/orslightly after.

In one variation, the system uses a decay time to determine when toremove words from the language model. The decay time can be based on howthe word appeared on the device display. For example, if a single wordoccurred in numerous places, prominently, or multiple times in a shortperiod, then the system can lengthen the decay time. Conversely, if aword appears once on the device display in a non-prominent position orin a very small font, the system can set a short decay time for thatword in the language model. When the system encounters another instanceof a word, such as on the device display or as recognized speech, thesystem can renew the decay interval for that word. For example, if thesystem extracts the word “apartment” from an initial screen on thedevice display and later the decay interval for the word “apartment” isabout to expire, a successful recognition of the word “apartment” in thespeech can renew the decay interval. The decay interval can also bebased on system capabilities such as processing speed.

The system can add identified words to a dynamic language model byrescoring an existing language model. In some cases, the systemidentifies other information besides text which provides insight intohow to better recognize the utterances or dialog. This other informationcan refer to external data, such as a website, Twitter address, or otherreference. If the connection is a computer network connection, such as aVoice over IP (VoIP) connection, the system can also gather informationfrom sources which are not visible during the call, but which wereviewed by one of the dialog participants earlier, such as a browserhistory. The system can extract from the captured text references toexternal data, retrieve at least part of the external data, identifydata of interest in the parsed data, and add the identified data ofinterest to the dynamic language model.

Then the system can recognize the recorded utterance using the dynamiclanguage model (210). In another aspect, the system identifies a user inthe dialog and saves the dynamic language model as a personalizeddynamic language model associated with the identified user. For example,if the system recognizes that a user is frequently involved inutterances or dialogs on a particular theme or topic, the system cansave a personalized language model tuned to a particular vocabularyunique to that user. The system can then retrieve another spoken dialogincluding the identified user, load the personalized dynamic languagemodel associated with the identified user, and recognize the secondspoken dialog using the personalized dynamic language model.

FIG. 3 illustrates an exemplary system configuration 300. The user 302has a dialog with a customer service agent 304 over a communicationsnetwork 306 such as the public switched telephone network or theInternet. As discussed above, a monolog or utterances from a single usercan easily substitute for the dialog. A server 308 records the dialog.The server 308 can also be integrated as part of the customer serviceagent's computing device. The server 308 passes the recorded dialog to aspeech recognition module 310 which can be separate or integrated intothe server 308. The speech recognition module 310 gathers input from thedisplay of the customer service agent 304, but can also examine thedisplay of the user 302. The system can record input from the display(s)and correlate the input to the recorded dialog. The speech recognitionmodule 310 identifies words in the captured text that are relevant tothe recorded dialog and adds the identified words to a dynamic languagemodel. The speech recognition module then recognizes the recorded dialogusing the dynamic language model to generate transcribed text 312.

FIG. 4 illustrates an exemplary display 400 of relevant information,such as that shown to a customer service agent. The display can includea customer name 402, customer address 404, product type 406 that is thesubject of the customer service call, and other items 408. In aspecialized application where the system knows exactly where and what toexpect on the device display, the system can focus narrowly on specificportions of the display for extracting relevant information for use in adynamic language model. In more fluid or general applications, thesystem can capture the entire screen or an entire window area on thescreen and analyze the captured portion for relevant information byoptical character recognition (OCR) and/or other suitable approaches.The system can also record user interactions and non-traditional inputsassociated with the displays, such as keystrokes, mouse clicks, mousemovement, gestures, device position and orientation information, etc.The system can then use this input in addition to information “scraped”from the screen to customize the dynamic language model.

Embodiments within the scope of the present disclosure may also includetangible computer-readable storage media for carrying or havingcomputer-executable instructions or data structures stored thereon. Suchcomputer-readable storage media can be any available media that can beaccessed by a general purpose or special purpose computer, including thefunctional design of any special purpose processor as discussed above.By way of example, and not limitation, such computer-readable media caninclude RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magneticdisk storage or other magnetic storage devices, or any other mediumwhich can be used to carry or store desired program code means in theform of computer-executable instructions, data structures, or processorchip design. When information is transferred or provided over a networkor another communications connection (either hardwired, wireless, orcombination thereof) to a computer, the computer properly views theconnection as a computer-readable medium. Thus, any such connection isproperly termed a computer-readable medium. Combinations of the aboveshould also be included within the scope of the computer-readable media.

Computer-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Computer-executable instructions also includeprogram modules that are executed by computers in stand-alone or networkenvironments. Generally, program modules include routines, programs,components, data structures, objects, and the functions inherent in thedesign of special-purpose processors, etc. that perform particular tasksor implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

Those of skill in the art will appreciate that other embodiments of thedisclosure may be practiced in network computing environments with manytypes of computer system configurations, including personal computers,hand-held devices, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, and the like. Embodiments may also be practiced indistributed computing environments where tasks are performed by localand remote processing devices that are linked (either by hardwiredlinks, wireless links, or by a combination thereof) through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote memory storage devices.

The various embodiments described above are provided by way ofillustration only and should not be construed to limit the scope of thedisclosure. Those skilled in the art will readily recognize variousmodifications and changes that may be made to the principles describedherein without following the example embodiments and applicationsillustrated and described herein, and without departing from the spiritand scope of the disclosure.

What is claimed is:
 1. A method comprising: identifying words with aspeech recognition model to yield identified words; removing a word ofthe identified words from the speech recognition model according to atleast an end time stamp assigned to the word to yield a modified speechrecognition model; and recognizing an utterance using the modifiedspeech recognition model.
 2. The method of claim 1, wherein the speechrecognition model comprises a first recognition model that is modifiedby associating the identified words to yield the speech recognitionmodel.
 3. The method of claim 1, wherein the identified words compriseinformation associated with a product or a service.
 4. The method ofclaim 3, wherein the identified words are presented on a display of anagent interacting with a user to assist in a purchase of the product orservice.
 5. The method of claim 1, wherein the identified words areassociated with social media content.
 6. The method of claim 1, whereinthe identified words represent a portion of a spoken dialog.
 7. Themethod of claim 2, wherein each word in the identified words is assigneda beginning time stamp and an end time stamp, and wherein theassociating of the identified words comprises adding each word to thespeech recognition model according to the beginning time stamp assignedto each word.
 8. A system comprising: a processing system including aprocessor; and a memory that stores executable instructions that, whenexecuted by the processing system, facilitate performance of operations,comprising: identifying words with a speech recognition model to yieldidentified words; removing a word of the identified words from thespeech recognition model according to at least an end time stampassigned to the word to yield a modified speech recognition model; andrecognizing an utterance using the modified speech recognition model. 9.The system of claim 8, wherein the speech recognition model comprises afirst recognition model that is modified by associating the identifiedwords to yield the speech recognition model.
 10. The system of claim 8,wherein the identified words comprise information associated with aproduct or a service.
 11. The system of claim 10, wherein the identifiedwords are presented on a display of an agent interacting with a user toassist in a purchase of the product or service.
 12. The system of claim8, wherein the identified words are associated with social mediacontent.
 13. The system of claim 8, wherein the identified wordsrepresent a portion of a spoken dialog.
 14. The system of claim 9,wherein each word in the identified words is assigned a beginning timestamp and an end time stamp, and wherein the associating of theidentified words comprises adding each word to the speech recognitionmodel according to the beginning time stamp assigned to each word.
 15. Anon-transitory machine-readable storage device, wherein thenon-transitory machine-readable storage device comprises executableinstructions which, when executed by a processor, cause the processor toperform operations comprising: identifying words with a speechrecognition model to yield identified words; removing a word of theidentified words from the speech recognition model according to at leastan end time stamp assigned to the word to yield a modified speechrecognition model; and recognizing an utterance using the modifiedspeech recognition model.
 16. The non-transitory machine-readablestorage device of claim 15, wherein the speech recognition modelcomprises a first recognition model that is modified by associating theidentified words to yield the speech recognition model.
 17. Thenon-transitory machine-readable storage device of claim 15, wherein theidentified words comprise information associated with a product or aservice.
 18. The non-transitory machine-readable storage device of claim17, wherein the identified words are presented on a display of an agentinteracting with a user to assist in a purchase of the product orservice.
 19. The non-transitory machine-readable storage device of claim15, wherein the identified words are associated with social mediacontent.
 20. The non-transitory machine-readable storage device of claim16, wherein each word in the identified words is assigned a beginningtime stamp and an end time stamp, and wherein the associating theidentified words comprises adding each word to the speech recognitionmodel according to the beginning time stamp assigned to each word. 20.The machine-readable storage medium of claim 15, wherein each word inthe identified words is assigned a beginning time stamp, and wherein thelinking the identified words comprises adding each word to the modifiedspeech recognition model according to the beginning time stamp.